Rational Design of Environmentally Friendly Carbon Nanotube Embedded Artificial Vesicle‐Structured Photocatalysts for Organic Pollutants Degradation
Bibliographic record
Abstract
Abstract Photocatalytic degradation is one of the most promising methods for addressing environmental issues without introducing pollution sources. Nonetheless, the majority of works are devoted to explore high‐efficiency semiconductor photocatalysts, but the potential environmental risks in their application are usually neglected. In this work, an environmentally friendly vesicle‐structured photocatalyst is rationally designed by employing magnetic layered double oxide‐hollow spheres@antimony tin oxide as a core, phospholipid as membrane, and carbon nanotubes (CNTs) as channels, yielding a multi‐structural material with robust organic pollutants photocatalytic degradation and mineralization ability. Materials characterization, computational modeling, and cytotoxicity tests suggest that under visible light irradiation, photogenerated charges can be rapidly generated and transfer in the core composite, and H2O2 can be effectively activated to generate hydroxyl radical for organic pollutants rapid degradation. The construction of phospholipid membranes and embedded CNTs not only maintains the photodegradation performance of the material, but also restrains its environmental risk. Such a synthetic approach advances the development of bionic photocatalytic materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".